text:
\section{Introduction}

During (dis)charge of lithium-ion batteries, the limiting physical process is the slow diffusion of lithium ions in active material particles~\cite{plett2015battery}.
The solid diffusion slows down the charging process and thus is a key factor impacting the fast-charging performance~\cite{Weiss2021}.
This mechanism has been well considered in physics-based models such as the widely-used Doyle-Fuller-Newman (DFN) model~\cite{Doyle1993}.
However, it is not sufficiently described in equivalent circuit models (ECMs).
ECMs can model the diffusion-induced overpotential by a series of resistor-capacitor (RC) pairs but cannot capture internal states such as the particle surface concentration.
Here, we propose a circuit network consisting of diffusion resistors and voltage sources to describe the solid diffusion process and resolve lithium-ion concentration distribution to assist internal state estimation and battery control.

The fundamental physical processes in lithium-ion batteries include lithium-ion diffusion in active materials, lithium-ion diffusion and migration in the electrolyte, electronic conduction in the solid conductive materials, and intercalation chemical reactions at the solid-electrolyte interface.
These processes are well understood and described in the DFN model through description by partial differential equations (PDEs), leading to accurate prediction of battery behavior and estimation of internal states.
Besides these basic physical processes, other physics such as thermal behavior and battery ageing from side reactions have also been modeled by mathematical equations on top of the DFN model.
However, discretizing and solving the coupled PDEs for battery composite materials are costly in terms of implementation and computation, and thus physics-based models are not feasible for real-time computation in state-of-art battery control systems.

In contrast, ECMs leverage well-established knowledge of electrical circuits (e.g., resistors, capacitors, and voltage sources) to mimic the cell behavior, in place of modelling underlying physics.
The standard electrical elements allow the ECMs to be solved in real time so that ECMs are widely used in battery management systems (BMS) for control purpose.
However, the loss of connection to physical processes also hinders ECMs in internal state estimation and weakens their prediction power.
Take the internal state of particle surface concentration as an example.
Conventional ECMs use a Warburg resistance \cite{plett2015battery} to represent the solid diffusion effect and then approximate it with multiple (usually two) RC pairs.
The RC pair network can only capture diffusion-caused overpotential but is not aware of the concentration distribution in the particle and the surface value.

The surface concentration is different from the average concentration as the diffusion takes time, especially under high dis(charge) rates.
It cannot be directly measured or estimated from the measured current and voltage data.
However, it is an important internal state in model-based battery control, for example, in the design of fast-charge protocols \cite{Yin2019} and in accurate estimation of state of available power (SoAP)~\cite{chaturvedi2010alg,Zheng2018} in fast-discharge scenarios.

Continuous efforts \cite{Rael2013,Scipioni2017,von_Srbik_2016,Merla2018a,Sato2019,Li2019a,Geng2021} have been made in bringing physics into circuit-based models for better state estimation and more accurate prediction.
The common idea is to use electrical analogies to represent the physical processes, including ageing mechanisms.
Most of these works focused on converting the DFN model to an equivalent circuit-based model, addressing the current distribution and charge transport processes.
The solid diffusion was addressed quite differently among the existing studies.
The first approach~\cite{Sato2019,Geng2021} is to keep the strategy in physics-based models, that is, to solve the diffusion equation using numerical techniques such as finite difference method and then use the solved surface concentration to calculate potential.
Alternatively, Li et al.~\cite{Li2019a} used a two-parameter polynomial approximation to the numerical solution to obtain the surface potential, which is a common approximation technique for single particle models \cite{Subramanian2005}.
Furthermore, Ouyang et al.~\cite{Ouyang2014} and Zheng et al.~\cite{Zheng2019} disregarded the concentration distribution but estimated the surface concentration from available approximate solutions to diffusion equation.
A circuit-style approach is to use circuit networks comprising of resistors and capacitors \cite{Rael2013,von_Srbik_2016} or of resistors and voltage sources \cite{Merla2018a} to resemble the solid diffusion process.

As discussed above, the transformation of the limiting solid diffusion process from physical description to circuit arrangement has still not been addressed in a satisfactory way.
The numerical solutions \cite{Sato2019,Geng2021} or approximate solutions~\cite{Li2019a,Ouyang2014,Zheng2019} inherit the complexity of addressing partial differential equations in physics-based models.
The circuit networks for solid diffusion in Merla et al.~\cite{Merla2018a} used the electrode open circuit potential (OCP) as the driving force for diffusion.
Th resistor-capacitor networks \cite{Rael2013,von_Srbik_2016} proposed promising analogies but needed further clarification (and what).
A well-designed circuit representation implementing the correct solid diffusion physics is needed for ECMs to be easily parameterized and implemented in a real-world BMS.
To this end, we propose a circuit network (\cref{sec:circuitnetwork}), consisting of artificial controlled voltage sources and diffusion resistors, to describe the solid diffusion process.
The artificial controlled voltage sources improves the previous work in our group \cite{Merla2018a}.
The proposed circuit network is defined as a new high-level circuit element called diffusion-aware voltage source (\cref{fig:fullECM}) and used together with resistors and capacitors to form shell ECMs (\cref{sec:shellecm}).
The proposed diffusion-aware voltage source and shell ECM are verified (\cref{sec:compspm}) against physics-based models (i.e., single particle model) run in an open-source battery-modelling toolbox PyBaMM \cite{Sulzer2021}.
The shell ECM also demonstrates its potential in predicting experimental data (\cref{sec:expval}).

\section{Equivalent circuit network for solid diffusion}

In this section, we present an equivalent circuit network for solid diffusion in active particles and then define the proposed equivalent circuit network as a diffusion-aware voltage source that can be used to configure physics-informed ECMs.
We start with brief introduction to the continuous description of the solid diffusion and spatial discretization by finite volume method in \cref{sec:diffusionPDE}.
Based on the spatially discretized diffusion equation, we devise the diffusion-aware voltage source (\cref{sec:circuitnetwork}) and proposed ECMs with the new voltage source (\cref{sec:shellecm}), called shell ECMs.

\subsection{Lithium-ion diffusion in active particles}
\label{sec:diffusionPDE}

Lithium-ion diffusion in active particles is described by a partial differential equation that is expressed as
\begin{align} \label{eq:soliddiff}
	\pdv{c}{t} + \div{\qty(- D_{\text{s}} \grad{c})} & = 0,
\end{align}
where $c$ is lithium-ion concentration as the field variable and $D_{\text{s}}$ is the diffusivity.
In a spherical coordinate system, it is reduced to
\begin{align} \label{eq:soliddiff_sph}
	\pdv{c}{t} + \frac{1}{r^2} \frac{\partial}{\partial r} \qty(-r^2 D_{\text{s}} \frac{\partial c}{\partial r}) = 0,
\end{align}
where $r$ denotes the radial coordinate (\cref{fig:discretization}).
The boundary conditions at the particle center and surface are, respectively,
\begin{align} \label{eq:bcs}
	\left. -D_\text{s} \frac{\partial c}{\partial r} \right|_{r=0} = 0 \quad \text{and} \quad
	\left. -D_\text{s} \frac{\partial c}{\partial r} \right|_{r=a} = j/F,
\end{align}
where $j$ is the interfacial current density flowing out the particle and $a$ is the particle radius.

Following the finite volume method, we divide the sphere into $N$ layers of equal thickness, as shown in \cref{fig:discretization}, and integrate \cref{eq:soliddiff_sph} over an arbitrary layer $n$ (control volume):
\begin{align} \label{eq:integralcons}
	\int_{r_{n-1}}^{r_n} 4\pi r^2 \qty[\pdv{c}{t} + \frac{1}{r^2} \frac{\partial}{\partial r} \qty(-r^2 D_{\text{s}} \frac{\partial c}{\partial r})] \dd{r} = 0.
\end{align}
The concentration variable $c$ in layer $n$ is considered constant and equal to the average concentration denoted as $c_n$.
We thus have
\begin{align} \label{eq:integradiff}
	\pdv{c_n}{t} \Omega_n + S_{n} J_{n} - S_{n-1}J_{n-1} = 0,
\end{align}
where $\Omega_n$ represents the volume of layer $n$, $S_{n}$ is the outermost surface area of layer $n$, and $J_n$ denotes the flux at the outermost surface:
\begin{align} \label{eq:disclayern}
	\Omega_n = \frac{4}{3} \pi \qty(r_n^3 - r_{n-1}^3), \quad
	S_{n}=4\pi r_n^2, \quad
	J_n = -D_{\text{s}}\pdv{c}{r} = -D_\text{s} \frac{c_{n+1} - c_{n}}{b},
\end{align}
where $b$ is the thickness of each layer.
Inserting \cref{eq:disclayern} into \cref{eq:integradiff}, we obtain the mass conservation for an arbitrary layer $n$:
\begin{align} \label{eq:discretized}
	\frac{\dd{c_n}}{\dd{t}} \Omega_n =
	\frac{c_{n+1} - c_{n}}{b/\qty(S_{n}D_\text{s})} - \frac{c_n - c_{n-1}}{b/\qty(S_{n-1}D_\text{s})}.
\end{align}
\begin{figure}
	\centering
	\includegraphics{figures/discretization.pdf}
	\caption{
		Schematics of discretizing an active particle of radius $a$.
		The particle is discretized into $N$ layers with layer number $n$ ranging from $1$ to $N$.
		The symbol $J_n$ denotes mass flux from layer $n$ to $n+1$, and $c_n$ is the lithium-ion concentration in layer $n$.
	}
	\label{fig:discretization}
\end{figure}

\subsection{Equivalent circuit representation}
\label{sec:circuitnetwork}

In conventional ECMs, solid diffusion in active particles is modeled by a Warburg element and a finite number of RC pairs~\cite{plett2015battery}, which cannot resolve the concentration gradient inside a particle.
Here we use electrical analogies to describe the solid diffusion and configure voltage sources and resistors in a shell structure as shown in \cref{fig:multivolts}, in place of the differential equation description \cref{eq:soliddiff_sph}.
\begin{figure}
	\centering
	\includegraphics{figures/multivolts.pdf}
	\caption{
		Schematics of shell circuit network (a) for solid diffusion description.
		The voltage $V_n$ ($n=1,2,\ldots,N$) of an arbitrary layer $n$ is a linear function of its internal state---lithium-ion concentration $c_n$, as shown in (b).
		The open circuit potential (OCP) of the particle depends on the surface stoichiometry (particle surface concentration normalized by the maximum concentration $c_\text{s,surf}/c_\text{s,max}$.
		The surface concentration $c_\text{s,surf} = 1.5c_{N-1} - 0.5c_N$ is a linear extrapolation of the concentrations in the outermost two layers.
		The real OCP relations for a positive electrode (PE) and negative electrode (NE) a LG50 cell \cite{Chen2020} are shown in (c) as an example.
	}
	\label{fig:multivolts}
\end{figure}

In the shell circuit network (\cref{fig:multivolts}), we have two electrical elements: resistors and controlled voltage sources.
The voltage source $V_n$ corresponds to the layer $n$ in \cref{fig:discretization} on a one-to-one basis.
According to Kirchhoff's voltage law, for an arbitrary circuit loop in \cref{fig:multivolts}a we have
\begin{align} \label{eq:kvl}
	V_{n} = V_{n+1} + \bar{I}_{n} R_{\text{d},n}.
\end{align}
Kirchhoff's current law states that
\begin{align} \label{eq:kcl}
	I_n & = \bar{I}_{n-1} -  \bar{I}_{n}.
\end{align}
Calculating $\bar{I}_{n-1}$ and $\bar{I}_{n}$ from \cref{eq:kvl} and inserting them into \cref{eq:kcl}, we have
\begin{align} \label{eq:circuitgov}
	I_n & = \frac{V_{n-1} - V_{n}}{R_{\text{d},n-1}} - \frac{V_{n} - V_{n+1}}{R_{\text{d},n}}.
\end{align}
For each layer $n$, we further attach an internal state variable $c_n$ with physical meaning of lithium-ion concentration to the controlled voltage source $V_{n}$ and define the controlled voltage as
\begin{align} \label{eq:controlledvolt}
	V_{n} = kFc_{n},
\end{align}
where $F$ is Faraday's constant and $k$ is a constant coefficient to convert units.
Substituting \cref{eq:controlledvolt} into \cref{eq:circuitgov}, we have
\begin{align} \label{eq:shelleq}
	\frac{I_n}{F} & = \frac{c_{n+1} - c_{n}}{R_{\text{d},n} / k} - \frac{c_{n} - c_{n-1}}{R_{\text{d},n-1} / k}
\end{align}
The current flow $I_n$ in \cref{fig:multivolts} suggests charge flux into voltage source $V_n$, and this is analogous to the lithium-ion mass flux into layer $n$.
To draw the analogy, we use Faraday's constant $F$ to make the units consistent:
\begin{align} \label{eq:connection}
	\frac{I_n}{F} = \frac{\dd{c_n}}{\dd{t}} \Omega_n,
\end{align}
indicating that the left-hand sides of \cref{eq:discretized} and \cref{eq:shelleq} are equal.
We can further define the diffusion resistances in the shell circuit network as
\begin{align} \label{eq:resistance}
	R_{\text{d},n} = \frac{kb}{S_{n}D_\text{s}} & = \frac{kb}{4\pi (nb)^2 D_\text{s}}
	= \frac{kN}{4\pi n^2 a D_\text{s}}.
\end{align}
Now \cref{eq:discretized,eq:shelleq} are completely equivalent.
Therefore, with the definition of controlled voltage source in \cref{eq:controlledvolt} and diffusion resistance in \cref{eq:resistance}, the proposed shell circuit network is equivalent to the spatially discretized diffusion equation.
We remark that the driving force of current flow in \cref{eq:controlledvolt} is the voltage difference and this reflects the concentration gradient is the driving force for diffusion. The governing equations for the shell circuit network is summarized in \cref{tab:goveeqs}.
\begin{table}
	\centering
	\caption{Governing equations for the diffusion-aware voltage source.} \label{tab:goveeqs}

	\begin{threeparttable}
		\begin{tabular}{@{}l@{}}
		\toprule
			\begin{minipage}{1.0\textwidth}
				\vspace{0.3em}
				1. Kirchhof's voltage law:
				\begin{align*} \tag{\ref{eq:kvl}}
					V_{n} = V_{n+1} + \bar{I}_{n} R_{\text{d},n}, \quad n=1,2,\ldots,N-1
				\end{align*}
				2. Kirchhof's current law:
				\begin{align*} \tag{\ref{eq:kcl}}
					I_n = \bar{I}_{n-1} - \bar{I}_{n}, \quad n=1,2,\ldots,N
				\end{align*}
				3. Internal state evolution:
				\begin{align*} \tag{\ref{eq:connection}}
					\frac{\dd{c_n}}{\dd{t}} = \frac{1}{F\Omega_n} I_n \qty(t), \quad n=1,2,\ldots,N
				\end{align*}
				4. Constitutive relation:
				\begin{align*} \tag{\ref{eq:controlledvolt}}
					V_n = k F c_n, \quad n=1,2,\ldots,N
				\end{align*}
				\vspace{0.1em}
			\end{minipage} \\
			\bottomrule
		\end{tabular}

	\end{threeparttable}
\end{table}

The integral form of the internal state $c_{n}$ of layer $n$ can be derived from \cref{eq:connection}:
\begin{align}
	c_{n} = c_{n,0} + \frac{1}{F\Omega_n} \int_{t_{0}}^{t} I_{n} \dd{t},
\end{align}
where $c_{n,0}$ represents the initial state that is given.
In some cases, the internal states can be changed to state of charge (SoC) as shown in \cref{sec:expval}.
Finally, the two current flows at the edges of the shell circuit network are specified as
\begin{align}
	\bar{I}_0 = 0 \quad \text{and} \quad
	\bar{I}_{N} = I_\text{in},
\end{align}
where $I_\text{in}$ is the portion of applied current $I_\text{app}$ for one active particle (detailed below).
These two expressions correspond to the boundary conditions (\ref{eq:bcs}) of the diffusion equation, respectively.

The shell circuit network as a whole can be considered as a high-level circuit element whose input is the applied current.
The output is the electrode potential
\begin{align}
	\text{OCP} = \text{OCP}\qty(c_\text{s,surf}),
\end{align}
where $\text{OCP}$ is the measured potential function for an active material (see \cref{fig:multivolts}c for examples) and the surface concentration is linearly extrapolated from those of the outermost two layers
\begin{align} \label{eq:surfconcen}
	c_\text{s,surf} = 1.5c_{N} - 0.5c_{N-1}.
\end{align}

In \cref{eq:resistance}, the number of diffusion resistors is up to $N-1$, but only one is unknown and needs to be determined.
As the innermost resistance is the largest, we take it as the unknown and baseline for calculating other diffusion resistances.
It can be expressed as
\begin{align} \label{eq:outerres}
	R_{\text{d},1} = \frac{kN}{4\pi a D_\text{s}},
\end{align}
suggesting the diffusion resistance is determined by the solid diffusivity.
Since the shell circuit network can describe the diffusion process, it involves a timescale that depends on the diffusion resistance.
We can define the timescale of the shell circuit network as the one of the continuous diffusion equation:
\begin{align} \label{eq:timescale}
	\tau = \frac{a^2}{D_\text{s}} = \frac{4\pi a^3 R_{\text{d},1}}{kN},
\end{align}
where the diffusivity $D_\text{s}$ is expressed in terms of diffusion resistance $R_{\text{d},1}$ from \cref{eq:outerres}.

\subsection{Shell equivalent circuit model}
\label{sec:shellecm}

In the previous section, we devise a circuit network for the solid diffusion.
This network can be considered as a new high-level circuit element that takes current as the input, stores internal states of concentration (or SoC), and gives surface OCP as the output.
Thus, we define it as a diffusion-aware voltage source symbolized by the controlled voltage symbol with an extra rotated square inside as shown in \cref{fig:fullECM}.
\begin{figure}
	\centering
	\includegraphics{figures/full-ecm.pdf}
	\caption{
		Shell ECMs equivalent to physics-based models---the DFN model (a) and Single Particle Model (b).
		Further simplified shell ECM (c) for easy experimental parameterization.
		The diffusion-aware voltage sources (with double circles) refer to the proposed shell circuit network \cref{fig:multivolts}.
	}
	\label{fig:fullECM}
\end{figure}

Based on the diffusion-aware voltage source, we propose an ECM in \cref{fig:fullECM}a to resolve the fundamental physical processes described in the DFN model and call it the comprehensive shell ECM.
The comprehensive shell ECM consists of two similar sub-circuits in the left-hand and right-hand sides, corresponding to the negative electrode (NE) and positive electrode (PE), respectively.
The upper resistors $R_\text{s}$ are used to model the electrical potential variation in the electrode thickness direction due to the electronic current flow in solid conductive materials.
The lower resistors $R_\text{e}$ are designed for the overpotential caused by lithium-ion diffusion and migration in the electrolyte.
Connecting electronic and ionic current flows is the intercalation and deintercalation chemical reactions on active particle surface, represented by an elementary circuit unit involving double layer capacitor $C_\text{dl}$, charge transfer resistance $R_\text{ct}$, and the diffusion-aware voltage source.
This elementary circuit unit resembles Randles circuit \cite{plett2015battery} but replaces the Warburg impedance with our diffusion-aware voltage source.

In \cref{fig:fullECM}a, we only show for demonstration three elementary circuit units in parallel at each side; the more elementary units, the higher resolution of current distribution and lithium-ion concentration/SoC gradient across the electrode thickness.
Therefore, the comprehensive shell ECM in \cref{fig:fullECM}a is especially suitable for high-current (dis)charges.
At the moment, these concerns are out of the scope of this study.
For interpretation of the diffusion-aware voltage source and easy parameterization, we simplify the shell ECM to the extent as shown in \cref{fig:fullECM}b.
We first disregard $R_\text{s}$ and $R_\text{e}$ as they are relatively smaller \cite{Geng2021} and omit the double-layer capacitance as it has very little impact except at very high frequencies \cite{plett2015battery}.
Consequently, the three elementary circuit units would behave exactly the same and thus can be integrated into one.

We remark that the reasoning behind the simplification from (a) to (b) is the assumption that all particles in different locations of the electrode behave in the same way, rather than using one particle to replace all the particles \cite{Marquis2019}.
Therefore, the applied current $I_\text{app}$ needs to be shared by all active particles, and the input current $I_\text{in}$ for the diffusion-aware voltage source should be calculated as
\begin{align}
	I_\text{in} = I_\text{app} / N_{\text{s}},
\end{align}
where $N_{\text{s}}$ is the number of active particles.

The simplified shell ECM in \cref{fig:fullECM}b captures all the physics of the single particle model (SPM)---lithium-ion diffusion in an active particle and charge transfer overpotential---and thus can fully reproduce the SPM simulation results.
Moreover. it involves $R_\text{e}$ to model the electrolyte resistance within separator that is generally ignored in standard SPM.

In practice, the measured cell data do not distinguish between the NE and PE.
Thus, we further simplify the shell ECM in \cref{fig:fullECM}b to the one in \cref{fig:fullECM}c.
The resistance $R_0$ responsible for instantaneous voltage change includes the transport resistance in the whole cell including electrolyte and electronically-conductive materials.
The unique diffusion-aware voltage source combines the diffusion effects of the PE and NE.
Compared to standard two-RC ECM with 5 parameters, this ECM has only 2 parameters to calibrate: $R_0$ and $R_{\text{d},1}$.
Moreover, it better describes the physics of solid diffusion.

Finally, the terminal voltages for shell ECMs in \cref{fig:fullECM}b and c are calculated, respectively, as
\begin{subequations} \label{eq:terminalvolt}
\begin{align}
	V_{\text{t}} & = \text{OCP}_\text{pe} - \text{OCP}_\text{ne} - I \qty(R_\text{ct,p} + R_\text{ct,n} + R_\text{e}), \\
	V_{\text{t}} & = \text{OCV} - I R_0.
\end{align}
\end{subequations}

\section{Comparison with physics-based model}
\label{sec:compspm}

This section aims to verify the developed shell circuit network by comparing its simulation results with results of physics-based model simulation.
Specifically, we first validate the equivalence between the diffusion-aware voltage source (\cref{fig:multivolts}) and solid diffusion equation and verify its numerical implementation in \cref{sec:shellvoltsour}, and then in \cref{sec:verifyshellECM} we extend the verification to the shell ECM (\cref{fig:fullECM}b).
For simplicity, we choose the SPM and run it within PyBaMM~\cite{Sulzer2021} as the reference solution.
In the PyBaMM simulation, we pick the LG\,M50 cell and its parameter set characterized by Chen et al.~\cite{Chen2020}.

\subsection{Diffusion-aware voltage source}
\label{sec:shellvoltsour}

To check the ability of the diffusion-aware voltage source in resolving lithium-ion concentration, we simulate the response of a cell subjected to a sequence of constant-current discharge, rest, constant-current charge, and rest and monitor the concentration variation in the PE as it it has lower diffusivity than the NE.
The test protocol can be confirmed by the current profile in \cref{fig:conc_grad}a, where the sign of current is positive on discharge.
From the PyBaMM simulation of SPM, we can retrieve the lithium-ion concentration profiles (\cref{fig:conc_grad}b) at different time instants, PE particle surface concentration evolution (\cref{fig:conc_grad}b), and PE open circuit potential (\cref{fig:conc_grad}d) and use them to check the shell circuit network response.
\begin{figure}
	\centering
	\includegraphics{figures/conc-grad.pdf}
	\caption{
		Verification of the shell circuit network by comparing with PyBaMM simulation results.
		Subplot (a) shows the current profile of the test of discharge, rest, charge, and rest, and subplot (b) shows the concentration $c_\text{p}$ profiles of the positive electrode (PE) at 4 time instants A-D pinpointed in (a).
		Subplots (c) and (d) show the surface concentration $c_\text{p,surf}$ of the PE active particle and the corresponding PE open circuit potential (OCP\textsubscript{p}).
	}
	\label{fig:conc_grad}
\end{figure}

For a fair comparison, the same parameter values as in SPM are used in the shell circuit network (\cref{fig:multivolts}a).
Specifically, it needs the following parameters: particle radius $a$, diffusivity $D_\text{s}$, initial concentration $c_\text{s,0}$, and the number of particles $N_\text{s}$.
The number of particles is determined by two more parameters:
\begin{align} \label{eq:particleno}
	N_\text{s} = \frac{\epsilon_\text{s} V_\text{s}}{ 4 \pi a^3 / 3},
\end{align}
where $V_\text{s}$ is the electrode volume and $\epsilon_\text{s}$ is the volume fraction of active material.

We pick four time instants (A-D) during the discharge and the following relaxation process and show the concentration spatial distribution for comparison in \cref{fig:conc_grad}b.
The surface concentration is calculated by \cref{eq:surfconcen} and presented in \cref{fig:conc_grad}c as a function of time.
Based on the surface concentration, the PE OCP is calculated according to the same function in SPM.
The agreement in \cref{fig:conc_grad}b-d verifies the equivalence between the proposed circuit network and the discretized diffusion equation as well as the numeric implementation of the circuit network.

\subsection{Charge transfer resistance}

With the diffusion-aware voltage source verified, we further explore the possibility of reproducing the SPM using an ECM (\cref{fig:fullECM}b) incorporating the diffusion-aware voltage source.
Besides the diffusion overpotential, another major contribution to terminal voltage loss in the SPM comes from the reaction overpotential.
The reaction overpotential in the SPM does not involve a timescale, and thus we simply use a resistor with varying resistance to reproduce the same effect.

To obtain this charge transfer resistance, we first calculate the reaction overpotential by reversing the Butler-Volmer equation \cite{Doyle1996} that describes the chemical reaction at the active particle surface.
According to Butler-Volmer equation, the interfacial current density $j$ is expressed as
\begin{align} \label{eq:butlervolmer}
	j = 2 j_0 \sinh \qty(\frac{F \eta}{2RT}),
\end{align}
where~$j_0$ is the exchange current density and~$\eta$ is the reaction overpotential.
In the SPM, the interfacial current density is determined from the applied current $I_\text{app}$ via
\begin{align}
	j = \frac{I_\text{app}}{A} = \frac{I_\text{app}}{3 \epsilon V / R} = \frac{I_\text{app}}{S_\text{a} \epsilon V},
\end{align}
where $S_\text{a} = 3 / R$ denotes the particle specific area (surface area to volume ratio), $\epsilon$ is the active material volume fraction, and $V$ represents the electrode volume.
The inverse Butler-Volmer relation is then formulated to express the reaction overpotential $\eta$, and the charge transfer resistance is then calculated as
\begin{align} \label{eq:rct}
	R_\text{ct} = \frac{\eta}{I_\text{app}} = \frac{2RT}{FI_\text{app}} \text{arcsinh} \qty(\frac{j}{2j_0}) =
	\frac{2RT}{FI_\text{app}} \text{arcsinh} \qty(\frac{I_\text{app}}{2 j_0 S_\text{a} \epsilon V}).
\end{align}
Here, the exchange current density~$j_0$ is written as
\begin{align}
	j_0 = m \sqrt{c_{\text{s,surf}} \qty(c_{\text{s,max}} - c_{\text{s,surf}}) c_{\text{e}}},
\end{align}
where~$m$~$\qty(\si{\ampere\per\square\meter \cdot \qty(\cubic\meter\per\mole)^{1.5}})$ is a rate constant of the charge transfer reaction, $c_{\text{s,max}}$ is the maximum lithium-ion concentration in the active particle, $c_{\text{s,surf}}$ is the surface concentration of the active particle, and $c_{\text{e}}$ is the lithium-ion concentration in the electrolyte.
In the SPM, the lithium-ion concentration in electrolyte is simplified as a constant, and thus the exchange current density $j_0 \qty(c_{\text{s,surf}})$ is only a function of particle surface concentration $c_{\text{s,surf}}$.
Finally, we remark that $R_\text{ct}$ in \cref{eq:rct} expresses the equivalent resistance of the charge transfer resistors of all particles arranged in parallel because the applied current is shared by all particles.

The inverse hyperbolic sine function $\text{arcsinh} \qty(x)$ approaches $x$ as $x$ tends to $0$, i.e.,
\begin{align}
	\lim_{x \to 0} \frac{\text{arcsinh} (x)}{x} = 1.
\end{align}
Hence, at low currents, we approximate $\text{arcsinh} \approx x$, and \cref{eq:rct} is further simplified as
\begin{align} \label{eq:rct-app}
	R_\text{ct} \approx \frac{RT}{F j_0 S_\text{a} \epsilon V}.
\end{align}
In this case, the charge transfer resistance is solely determined by the particle surface concentration.
The particle surface concentration is time-varying, so is the charge transfer resistance.

To check the effect of applied current amplitude, we calculate the charge transfer resistance at different C-rates as shown in \cref{fig:rct}.
In general, the resistance of the NE is one magnitude higher than that of the PE (caused by the reaction constant), and the effect of applied current is more obvious for the NE.
For both electrodes, the approximation at small currents overestimates the resistance.
\begin{figure}
	\centering
	\includegraphics{figures/resistance_ct.pdf}
	\caption{
		Charge transfer resistance in the positive ($R_\text{ct,p}$) and negative ($R_\text{ct,n}$) electrodes as function of surface concentration at different C-rates.
	}
	\label{fig:rct}
\end{figure}

\subsection{Shell ECM}
\label{sec:verifyshellECM}

We further compare the shell ECM (\cref{fig:fullECM}b) against the SPM.
For the shell ECM, the electrolyte resistance $R_\text{e}$ is set to be null, and charge transfer resistances $R_\text{ct,n}$ and $R_\text{ct,p}$ are determined through \cref{eq:rct}.
Three test profiles are considered: the one in the previous section, Galvanostatic Intermittent Titration Technique (GITT), and constant-current discharges at varying current rates.
\cref{fig:comp-spm} shows the terminal voltage obtained from running the shell ECM and PyBaMM SPM simulation with the same parameters.
It is shown that the shell ECM can completely reproduce the SPM simulation results.
\begin{figure}
	\centering
	\includegraphics{figures/comp-spm.pdf}
	\caption{
		Comparison between shell ECM results and results from SPM simulation in PyBaMM in terms of terminal voltage for three test protocols.
	}
	\label{fig:comp-spm}
\end{figure}

\section{Experimental validation}
\label{sec:expval}

This section aims to validate the proposed shell ECM by comparison with experimental measurements of LG INR21700-M50T (LG\,M50T) cells.
In a real-world BMS, the cell terminal voltage can be measured, but it is impractical to differentiate the contributions from the two electrodes.
Thus, we choose the simplified shell ECM involving a single diffusion-aware voltage source as shown in \cref{fig:fullECM}c.

For parameterization and validation, GITT and constant-current discharge tests were performed respectively on LG\,M50T cells.
Before these tests, the cells were fully charged through the constant-current/constant-voltage protocol at a charge rate of 0.3\,C to a maximum voltage of 4.2\,V until a cut-off current of C/100, followed by a two-hour rest.
The GITT tests consists of 25 repeating cycles of a discharge pulse at a 1\,C rate for 144 seconds followed by a one-hour rest.
The nominal cell capacity is \SI{5}{\ampere\hour}, and thus each discharge pulse consumes \SI{0.2}{\ampere\hour}.
Constant-current discharge tests were conducted at rates of 0.4\,C and 2\,C until the lower voltage limit of 2.5 V was reached.
All tests were performed at a constant temperature of \SI{25}{\degreeCelsius}.
The experimental designs can be found in more details in Kirkaldy et al. \cite{Kirkaldy2022}.

The shell ECM is first parameterized against the GITT data and then is used to predict the cell performance under constant-current discharge conditions, in comparison with the measurements.

\subsection{Parameterization}

For the shell ECM in \cref{fig:fullECM}c, there are only two parameters to be calibrated: the ohmic resistance $R_0$ and diffusion resistance $R_\text{d,1}$ between the first and second layers (\cref{fig:multivolts}).
These two parameters are determined from the GITT data through the layered method \cite{Jackey2013} as sketched in \cref{fig:layer_method}a.
For each cycle of a discharge pulse followed by a rest, the two parameters are assumed constant and are identified separately.
The ohmic resistance $R_0$ is determined from the instantaneous drop and increase of the terminal voltage $V_0$ divided by the applied current, and the average of the two $R_0$ values is recorded.
The diffusion resistance $R_\text{d,1}$ is fitted by minimizing the difference between the experimental measurements and model predication of the terminal voltage (i.e., least-squares method).
The least-squares fitting is only applied to the relaxation section in \cref{fig:layer_method}a where the ohmic effect vanishes.
\begin{figure}
	\centering
	\includegraphics{figures/layer_method.pdf}
	\caption{
		The two shell ECM parameters are determined by the layered technique for each discharge-rest cycle.
		(a) A typical discharge pulse at a 1\,C rate followed by a rest where the two parameters are assumed constant.
		The ohmic resistance $R_0$ is calculated from the instantaneous changes ($V_0$) of the terminal voltage ($V_\text{t}$), while the diffusion resistance $R_\text{d,1}$ is obtained by fitting the experimental data of the relaxation part during which the diffusion overpotential $V_1$ diminishes.
		(b) Calibrated parameters for 25 discharge and rest cycles and the second-order polynomial fitting of these parameters as function of the state of charge (SoC) for model predication in the following constant-current discharges.
		The SoC of each point takes the mean value of the SoC range that the corresponding discharge-rest cycle goes through.
	}
	\label{fig:layer_method}
\end{figure}

The calibrated parameters for each discharge-rest cycle by the layered method are then presented as discrete points in \cref{fig:layer_method}b and c.
Each discharge pulse undergoes \SI{144}{\second} at a 1\,C rate and consumes $4\%$ of the cell nominal capacity.
Thus, there are in total 25 discharge pulses and corresponding parameter sets.
The cell bulk SoC is calculated by coulomb counting and its variation during each discharge pulse is recorded.
The mean value of the two SoC limits of each pulse is then used to indicate the SoC level associated with the calibrated parameters.

With the two parameters identified for each cycle, the best-fitting voltage curve is shown in comparison with the experimental curve in \cref{fig:exp-gitt-volt}a.
Also, the equilibrium points after relaxation are taken to serve as the OCV.
To evaluate the calibration, we plot the root mean square error (RMSE) for each discharge-rest cycle in \cref{fig:exp-gitt-volt}b:
\begin{align}
	\text{RMSE} = \sqrt{\frac{1}{J} \sum^{J}_{j=1} {\qty(V_{\text{exp},j} - V_{\text{pre},j})^2}},
\end{align}
where $j$ represents the $j$-th point and $J$ is the number of data points.
Here we present two RMSE curves: one takes data of the whole discharge-rest cycle, indicating the overall error of calibrating $R_0$ and fitting $R_\text{d,1}$; the other only uses the data of the rest section, indicating the least-squares fitting error.
Compared to the overall error, the fitting error of $R_\text{d,1}$ is quite slight, implying that the relaxation process through diffusion is well modeled.
Another observation in \cref{fig:exp-gitt-volt}b is that the fitting errors in the low SoC area (pulses 23--25) are relatively higher than those of other pulses.
This observation suggests that the poor estimation accuracy is not likely to be improved by considering the solid diffusion and resolving surface concentration, inconsistent with the conclusion by Ouyang et al.~\cite{Ouyang2014}.
\begin{figure}
	\centering
	\includegraphics{figures/exp_gitt_tervol.pdf}
	\caption{
		(a) Best-fitting curve against measured data with calculated $R_0$ and fitted $R_\text{d,1}$.
		(b) Root mean square errors of the best-fitting curve for each discharge-rest cycle.
		(c) Diffusion timescales derived from the fitted diffusion resistances.
		For the timescale estimation, we explored the effect of layer number; otherwise, $10$ layers are used elsewhere.
	}
	\label{fig:exp-gitt-volt}
\end{figure}

According to \cref{eq:timescale}, the timescale of solid diffusion is related to the diffusion resistance $R_\text{d,1}$.
Here, the diffusion resistance $R_\text{d,1}$ is fitted from experimental data and reflects the collective effect of diffusion in both electrodes.
In general, the NE has diffusivity one order of magnitude higher than that of the PE \cite{Chen2020}, that is, the diffusion in the PE is the limiting process.
We thus assume the fitted resistance mainly indicates the PE diffusion timescale, and the timescale is then estimated as
\begin{align} \label{eq:timescale-fit}
	\tau_\text{fit} = \frac{4 \pi a^3 F c_\text{s,max} N_\text{s}}{N} R_\text{d,1},
\end{align}
where $N_\text{s}$ is the particle number calculated by \cref{eq:particleno} and $N$ is the layer number of the shell ECM.
Note that \cref{eq:timescale-fit} is different from \cref{eq:timescale} because the internal state is now changed to the SoC where cell nominal capacity is used for normalization and the current is applied to a single particle.
The fitted timescale $\tau_\text{fit}$ would not change too much if the parameter set of NE is used, in view of the consistency between both electrodes in terms of capacity ($\sim c_\text{s,max} a^3 N_\text{s}$).

The timescales at different SoC levels basically remain at the same magnitude and generally higher than the value of \SI{1.89}{\hour} calculated from PE particle radius and diffusivity of a LG\,50 cell \cite{Chen2020}.
We remark that the difference caused by number of layers is negligible, confirming that the layer number does not change the physics but a parameter affecting the solution accuracy.

\cref{fig:exp-gitt-soc}a shows the evolution of the SoC of each layer as well as the cell bulk SoC at the fitted diffusion resistance.
The bulk SoC decreases during the discharge pulse and then remains unchanged during the rest period; this pattern is repeated from cycle to cycle.
A typical cycle (the fifth) is then demonstrated in \cref{fig:exp-gitt-soc}b.
The outermost layer (10th) is characterized by the rapidest decrease of SoC in the beginning of the discharge pulse, while the innermost layer basically does not lose any lithium, forming a concentration gradient inside the particle.
After the current is cut off, the concentration in the outermost layer immediately starts to increase while lithium keeps flowing from the inner layers to the outer layers to reach an equilibrium eventually.
The derivative of the concentration with respect to time can be seen by the current flowing out from each layer in \cref{fig:exp-gitt-soc}c.
The current $I_{10}$ flowing out the 10th layer contributes most to the discharge pulse, and then comes the 9th layer.
The sudden drop of $I_{10}$ at \SI{256}{\minute} from a positive (discharge) value to a negative (charge) one explains the sharp reversing of SoC\textsubscript{10} in \cref{fig:exp-gitt-soc}b.
The difference between SoC\textsubscript{10} and the bulk SoC is responsible for the diffusion overpotential, considering that SoC\textsubscript{10} is close to the surface SoC.
\begin{figure}
	\centering
	\includegraphics{figures/exp_gitt_soc.pdf}
	\caption{
		Evolution of the SoC of each layer and the bulk SoC (a), and the fifth discharge-rest cycle is magnified in (b) for clarity.
		The corresponding current outflow from each layer is plotted in (c), where the sign of current is opposite to that in \cref{fig:multivolts}.
	}
	\label{fig:exp-gitt-soc}
\end{figure}

\subsection{Prediction}
In this section, we use the calibrated two parameters to predict the cell performance in constant-current discharge tests and compare with the experimental data.
The two datasets in \cref{fig:layer_method}b and c, expressing the dependency of $R_\text{d,1}$ and $R_0$ on the cell SoC, are first fitted by quadratic functions using the least-squares method.
The two best fits (green solid lines) are then used in the shell ECM.
Since these parameters are obtained from GITT data at a 1\,C discharge rate, we pick two other rates---0.4\,C and 2\,C for the constant-current discharges.

The model prediction are presented in \cref{fig:exp-cc-pred}a, in comparison against experimental measurements.
The estimation error, difference between the predicted terminal voltage and measured voltage ($V_\text{pre} - V_\text{exp}$), is found to be restricted into the magnitude of \SI{0.1}{\volt}, as shown in \cref{fig:exp-cc-pred}b.
The predication captures the trend of the terminal voltage variation at a low and a high rate, suggesting that the two overpotentials caused by ohmic resistance and diffusion contribute most to the terminal voltage variation.
The predicated voltage curves are not as smooth as the the measured curves at the two discharge rates; this is because the OCV curve as the baseline is not perfectly smooth (\cref{fig:exp-cc-pred}a)---it is linearly interpolated based on the 25 discrete OCV data points from the GITT data (\cref{fig:exp-gitt-volt}a).
After the overpotential deductions, the predicted voltage curves at both rates preserve the feature of the OCV curve, leading to the difference between the predicted and measured data such as the big gap in \cref{fig:exp-cc-pred}a at the 2\,C rate.
\begin{figure}
	\centering
	\includegraphics{figures/exp_cc_pred.pdf}
	\caption{
		Comparison between shell ECM predication and experimental data.
	}
	\label{fig:exp-cc-pred}
\end{figure}

\section{Conclusion}

We proposed a circuit network (\cref{fig:multivolts}) to describe the lithium-ion diffusion in an active particle, and the proposed circuit network is theoretically and numerically proven equivalent to the discretized diffusion equation based on finite volume method.
We define the whole circuit network as a new high-level circuit element, called diffusion-aware voltage source and sketched as the conventional controlled voltage source with an extra inner rotated square (\cref{fig:fullECM}).
This diffusion-aware voltage source acts as an alternative to the conventional voltage source and Warburg impedance in ECMs.

Inside the proposed circuit network, the voltage of each layer presents linear dependence on the concentration/SoC to reflect the driving force of concentration gradient, different from the usage of real OCP in Merla et al.~\cite{Merla2018a} that indicates the electrochemical potential as the driving force.
The real OCP relation is applied to the surface concentration to act as the output of the diffusion-aware voltage source.
The design of the diffusion-aware voltage source has been verified by comparison against SPM simulation within PyBaMM, and its applicability in ECMs has been demonstrated through comparison against experimental data.

The most significant benefit of the diffusion-aware voltage source is the track of concentration/SoC distribution, especially the surface concentration for SoAP estimation.
The equivalence between the proposed circuit network and discretized diffusion equation guarantees the accuracy of resolving lithium-ion concentration to the level of physics-based models.
The value of the work lies in the circuit analogy to the spatially discretized diffusion equation and boundary conditions, transforming the continuous description in physics-based models into understanding of well-established knowledge/behavior of circuit elements.
The new diffusion-aware voltage source is in essence a system of ordinary differential equations that require numerical techniques comparable to conventional ECMs.

\section*{Acknowledgements}
The research leading to these results has received funding from the Innovate UK through the WIZer Batteries project (grant number 104427) and EPSRC Faraday Institution Multi-Scale Modelling project (EP/S003053/1, grant number FIRG003).

redpajama_set_name: RedPajamaArXiv


On December 20 when an expert committee of the Union environment ministry visited Visakhapatnam, tribal areas of the district observed bandh. Anti-mining protestors from different tribal groups and political parties, except the ruling Congress, blocked roads leading up to the Eastern Ghats. People wielding bows and arrows guarded some of the hilltops proposed for bauxite mining. The committee, which had come to study the socio-economic and environmental impact of the mining project, cancelled its visit to hills by road and returned after conducting an aerial survey. Anger is simmering in the tribal areas of the district—also known as Agency area—which fall under the Schedule Five category. Tribal groups and civil society organisations allege the Andhra Pradesh government is flouting all regulations and a Supreme Court judgement, meant to protect constitutional rights of tribals over their traditional land and resources, to appease two companies. Jindal South West Holdings Limited (JASWHL) and ANRAK, a joint venture of the government of Ras Al Khaimah from the United Arab Emirates and Andhra Pradesh-based Penna Cements, are setting up aluminium plants in the state for which bauxite is the raw material.
"The government acquired environmental clearance for the mining project despite our strong opposition and without consent from gram sabhas," says K Surendra, district president of Girijana Sangham, the tribal wing of CPI(M). According to a study by the Centre for Economic and Social Studies (CESS) in Hyderabad, the government plans to mine 27 hills in Araku Valley, Ananthagiri, Chintapalli and G K Veedhi blocks. The project will affect at least 270 villages of Kondh, Bagata, Konda Reddi and Samanta among other tribes, who traditionally inhabit the Agency area.
The Centre had stalled the project in 2010. It was the time around when people's agitation had forced the Union Ministry of Environment and Forests (MoEF) to cancel clearance to Vedanta's bauxite mining project in Niyamagiri hills in Odisha. But the recent visit by the MoEF committee shows fresh efforts are being made to revive the project. People feel betrayed as the committee's visit came despite assurances by Union tribal welfare minister Kishore Chandra Deo, who hails from the Agency area, to take up the matter with MOEF and get the clearances cancelled.
Andhra Pradesh holds 600 million tonnes of bauxite, which, according to the Geological Survey of India, is 21 per cent of the country's total reserve. More than 90 per cent of this reserve is concentrated in six deposits in the Eastern Ghats of Visakhapatnam (see map, table). The deposits falls in the Agency area, which is part of reserve forests and has a rich biodiversity. Environmentalists have long been pressing MoEF to declare the region a biosphere reserve.
But industries eyes the land. In fact, the state had initiated plans to tap the resource during the Telugu Desam Party regime in 2000. The then chief minister N Chandrababu Naidu had used every trick in the book to attempt amendments to laws governing land rights of tribal people and the Constitutional protection of Scheduled Areas, but had failed. The Congress regime under late Y S Rajasekhara Reddy, who came to power in 2004, managed to get around the problem.
The first MoU was signed with JSWHL in 2005 and the next with Ras Al Khaimah in 2007. Jindal would set up a refinery to produce 1.4 million tonnes of alumina a year, mainly for exports, with an investment of Rs 900 million. ANRAK will produce one million tonnes of alumina and 250,000 tonnes of aluminium a year with US $2 billion investment.
Since the Supreme Court's Samata Judgement in 1997 prevents any private company from owning or leasing land for mines or industry in the Scheduled Areas of the state, the MoUs are made tactfully. The judgement had allowed public enterprises and cooperative societies solely run by the tribes to mine in accordance with the forest and environmental laws. So the Andhra Pradesh Mineral Development Corporation (APMDC), a public undertaking, will mine bauxite on behalf of the companies. "We will supply ore to Jindal from Araku blocks and to ANRAK from Jerrela block," says K Rajasekhara Reddy, general manager of APMDC. The companies will bear the cost of mining. APMDC will receive 1.25 times of the royalty and 0.5 per cent of the annual turnover of the companies. Mining for ANRAK will start before June this year, he says. To avoid land transfer regulations, which prohibit transfer of tribal land to non-tribals, the companies are setting up plants outside the Scheduled Areas.
"The government is considered the trustee of the natural resources. But APMDC is acting as a front company of the industries," says G Srinivas of Samata, a non-profit whose petition is the precursor of the Samata Judgement. "The proposed mining plan negates the spirit of this judgment," says E A S Sarma, former tribal commissioner.
Besides, the government is yet to settle forests rights of the tribals. Around 160,000 ha of the Agency area belongs to the tribals as per the Forest Rights Act but the government has so far granted pattas (land titles) for around 16,000 ha. "In 2009, it made grand announcement about giving pattas for 40,500 ha, but nothing has been done so far. If mining is allowed before settling pattas, thousands of the tribals will be thrown out of their homes," says Narasinga Rao, district secretary of CPI(M).
APMDC insists that 26 per cent of the revenue earned from the project will be spent on development of the tribal communities as profit-sharing. It claims mining will generate employment for 20,000 people in the area, though an RTI query by Samata reveals that APMDC does not have details of the possible employment generation. However, it has selected 215 youths from the proposed mining areas and is providing them mining training and a monthly salary of Rs 7,500. Officials say the expenses will be borne by the companies.
The committee insists the environment impact assessment (EIA) reports for the mining projects were prepared by prestigious institute, Indian Council of Forestry Research and Education. They elaborately deal with socio-economic issues of the tribals. But an analysis of these EIA reports by the Centre for Economic and Social Studies (CESS) shows the EIAs have ignored the social and livelihood issues of the tribals.
A visit by Down To Earth to some of the tribal villages proposed for mining confirms this. Most villagers in the Agency area are small farmers with land holdings up to a hectare. They cultivate coarse rice, jowar, pulses and varieties of beans and coffee. Many eke out a living by collecting forest produces.
India's first tribal growers' organic coffee brand was launched in the Araku Valley in 2007. The premium organic Araku Emerald brand of coffee has buyers from around the world. Thousands of tribals in the Agency area are engaged in coffee cultivation as farm hands or small farmers. Mining would affect over 1,400 ha of coffee plantations in the valley. "If the government can give us the same forest, same hill and same kind of coffee plantations as compensation, then maybe I'll move out," says Kora Bheemanna, a Nooka Doora tribe of Beesupuram village, on the slopes of Galikonda hills. He has half-a-hectare plantation, which fetches him around Rs 20,000 every season.
"Once mining begins on their land, they will all turn labourers," says B David, a Kondh youth working with Primitive Tribal Groups Welfare Association in Araku. Going by APMDC, the Araku deposits will last only 15 years. Where will the cultivators-turned-labourers go after that, he asks.
Further, with lush green forests, streams and waterfalls, Araku Valley is emerging as a tourist destination. "Araku is the state's Kashmir," says Poojari Gasi of the Tourism Tribal Contract Labourers' Union of Borra Cave. The cave, considered the deepest in the country and lined with stalactites and stalagmites, is the main attraction. It is just three kilometres from the proposed mining site in the Ananthagiri hills.
Since bauxite exists in thin but extensive layers on top of the topography, it has to be mined open cast. This would require clearing of a large forest cover and blasting off the hilltops up to a depth of 100 feet. Its impact on the sensitive ecosystem of the Eastern Ghats will be irreversible. At least six rivers flowing across the hills may dry up or reduce to a trickle. This will affect major reservoirs which are the only source of irrigation and drinking water for Visakhapatnam and Vizianagaram districts.
Mountains of the Agency area and Odisha's Niyamagiri are parts of the Eastern Ghats. One wonders why MoEF cancelled mining Niyamagiri and is yet allowing mining in the Agency area.
Ministry of environment and forestry must be the strongest backbone for indian economy but it has always been highlighted to overule the environmental issues.Inspite of helping and supporting the farmers and gram sabha,the ministry is spoiling the environment.The MOEF management is so pathetic that it takes years to submit reports on the flora and fauna reserve.
Not only the project is completely illegal because it violates Samata's judgement and would require the consent of the local communities, but economically it is insane for India to hand its minerals to private companies, mainly for export, when all the other world economic power are on the contrary trying to secure their reserves of these finite resources... The so-called expert committee held a public hearing in Vizag but the only objective was to convince the people they had to accept the project. What is the point of a consultation if the decision is already made? How can they ignore the bandh and the general opposition all over Araku?
I read your article with great interest. In addition to the very important issues brought up by you I would like to mention the TRANSPORTATION NIGHTMARE that will come with the mining of the Jerala hills. The ANRAK website has an innocuous sentence at the end of it saying: "A newly laid road is envisaged which will be traveling for 15 km in forest land starting from Jerrela village to the state highway near Pederu. From there it is connected to NH5 for onward movement to the proposed plant location." This is probably written by someone who has little idea of the roads of the area. I could not figure out how they will reach Makavaripalem from Chintapalle via Paderu and NH5! It is obvious that they intend to come down via Lambasinghi and take a left before Narasipatnam. I understand that they have already started making a road diagonally from a few kms before Narsipatnam to Makavaripalem. Which ever route they take the statistics are mind boggling. They have indicated that they wish to move 4.5 Million Tons of Bauxite per annum. This figure throws up some interesting logistical statistics: Considering the narrow ghat roads - if they use a 16T tipper truck the stats are as follows: Quantity of Bauxite to be moved per year is 4,500,000 tons (4.5m Tons) Truck loads per annum with 16T Tipper: 281,250 That works out to 770 Truck loads per day travelling in one direction - I shudder to calculate the figures in both directions Divide that by 8 hours a day and you get 96 Trucks per hour And that translates to 1.6 Trucks every 60 seconds! If they use the huge 25 Ton multi-axle trucks instead of the smaller 16 Ton ones it will still be 1 Truck every sixty seconds! So if we were standing on one side of the road and wished to cross we would have only seconds between trucks! (If they travel only in one direction) There will be hundreds of human and cattle road deaths a year! If APMDC was shifting such quantities of material, no one else will be able to use the road. What will happen to the hundreds of village santhas all along the route? How will public transport such as buses, auto rickshaws and farm produce trucks ply? How will animals - cows, buffaloes and sheep - be driven to the santhas? Then there is the issue of tree felling. Road widening between Narsipatnam and Downuru - the foothills of the Chintapalli ghats - has already caused hundreds of large old trees to be felled. If the ghat sections are widened there could be more than 20,000 trees that will be lost. A crime against humanity during these days of global warming. On one hand we declare targets to increase tree cover and on the other hand we chop off thousands of huge old trees. Then consider vehicular dust pollution. Each truck during its journey from mine head to the refinery and back will throw up 200 to 300 kgs of mud and dust into the air. The amount of dust thrown into the air in a year will be a whopping 80,000,000 tons (80 million tons). The road and the roadside trees will be red! It will become unbearable for anyone to live and work along the roadside. Plus add to that vehicular exhaust. I wonder if this has been studied by our friends in the PCB. Lets talk about water. ANRAK want to tap 5 million gallons per day from the Yeleru Left Main Canal for their refining unit. I understand that they have sanction for 10 m gallons. But let us take the lower figure. Now, 5m gallons per day is equal to 5,000,000 X 4.5 = 22,500,000 liters per day! Estimating that each person needs around 100 liters of water per day for cooking, drinking, bathing and washing - this water can be used by 225,000 persons each day! Equivalent to the population of a standard town. I mention this to illustrate the enormity of consumption. In reality the water from the Yeleru canal is meant predominantly for agriculture and partly for domestic use. There are other huge impacts - and we all know it already - most importantly on the ecology of the hills and on the livelihoods of the Adivasis. This giant in our neighborhood will make life unlivable for thousands of people and will deprive them of resources as never before. An entire generation will be affected and after the hills have been raped it will take a thousand years to recover - if it recovers at all.
If its in a scheduled area, then Governor has enough powers to cancel transfer of land. And for that, he is not even bound by the aid and advice of the council of ministers.
Schedule 5 says "(2) The Governor * * * may make regulations for the peace and good government of any area in a State which is for the time being a Scheduled Area.
(a) prohibit or restrict the transfer of land by or among members of the Scheduled Tribes in such area"
If somehow possible and also if its seems correct, then this should be communicated to the protestors. Maybe the Governor can hear better than the minister.


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I'm curious why you would pay a performance fee on an account if you already own the EA?
Hello nwboater, the best answer I can give is I'm looking for stability in gaining profits every single year. At darwinex the darwin competition is super tight, very talented traders, fund managers, EA developers compete there to attract investors to invest on them. This is really great for investors, because only the winners come up to the surface, whoever lose and can't make a profitable portofolio will sink. And the winner always provide profitable performance for me every year.
With using EA we don't know the secret sauce behind it, if the EA fail and only losing money we only will stop to run the EA, throw it into trash and buy another EA (this circle will always be like this, forever!). I'm tired to do that (buying EA again and again) because it's expensive and frustrating. For example Belkaglazer EA developer, Algotradesoft EA developer and YPY Amet EA developer already join darwinex and their darwins are in the Top 20 Darwin list right now, and you know what? I'm free to enjoy the profit from their darwin, I don't need to buy the EA (I can safe alot of money from this and still enjoy nice income from their EAs performance). About the fee, the fee is paid every 3 months if the darwin generate profit for you and the profit sharing is 80% for you and 20% for Darwin provider, if the darwin fail to make a profit you don't need to pay.
Darwinex is not a PAMM, it has safe risk management algorithm to protect your capital, and something like this never exist before. By the way, I have no intention to promoting darwinex here. I'm pure an investor there, I've been there for years now and the profit is really good, so why not to share it here and asking FXAutomater to join too..
Thanks for the thorough explanation.
I think you have an interesting, and most likely highly profitable approach to invest in the top providers at Darwin. One of them is Thales which I previously had as a very profitable Signal. Unfortunately it's no longer available as a Signal and I think also not at Darwin because the AUM is getting so large.
You make some good arguments for going with Darwins as opposed to EA's. Would you consider starting a thread in the managed accounts section here? It would be interesting to get others involved in the discussion and compare notes and preferred vendors.
starting balance of $1000 .. this looks like maybe the only account which has not had increased risk used to build its growth ..
My strategy is very simple, I just pick 10 best darwin (Diversification purpose) with low divergent and good profit from 20 TOP Darwin and invest equal amount of money to each darwin, the last step to do is let it run for a year whatever happened (you can call it "Set and Forget"). With this approach in 2 years I made 110% profit with DD only 6%.
The EA uses recovery in drawdown, increasing the lot size by a factor of 1.04 or 1.05 for each new trade, until the drawdown is not fully compensated. The idea is, when it catches a good strong movement it happens with many trades with increased lot size, which in a good day brings huge profit. Of course, it can be used successfully without recovery, but the recovery just works great for this type of impulse following systems.
could you please be so kind and post a backtest of eu with fixed lot size from the below account.
i would like to see how close bt matches to forward.
but i am afraid trades dont match your forward demo account. it trades open bar i assume as most all your bots do, so i cant figure out why trades dont match since they should match really close.
for example, feb 16 this year = the demo i refered to had a sequence of losses, backtest shows only 1 loss.
but the demo had quite a number of wins also on feb 16 which the backtest has none, zero zilch.
that does not make sense to me. why is there such a huge difference ??


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Why SJTU
Heritage of excellence
Shanghai Jiao Tong University (SJTU) was founded in 1896 as Nanyang College. Carrying on the tradition of "high starting points, a solid foundation, strict requirements, and keenness on practice and innovation," SJTU has nurtured over 200,000 talents who have been working all over the world. Among them are famous statesmen such as Jiang Zemin and "The Father of Chinese Aerospace," Qian Xuesen. There are other famous scholars such as the great master of mathematics, and winner of the first National Supreme Scientific Technology Award, Wu Wenjun and also, Wang Zhenyi who was the winner of the Kettering Prize for cancer research. Over 200 members of the Chinese Academy of Sciences and the Chinese Academy of Engineering are SJTU alumni.
The university enjoys national prestige and is a leading research-oriented and comprehensive university in China. A great number of SJTU alumni have made prominent achievements in recent years in various fields including IT, finance, manufacturing, etc.
Great facilities and vibrant campus life
With SJTU situated in Shanghai, the most dynamic city in East Asia, its students and faculty enjoy both serene campus life and abundant offerings of this culturally rich and diverse city. The nearby Zizhu Science Park, with over 100 enterprise research centers, forms a strong atmosphere for innovation and R&D as well as promoting vigorous interaction between the academic community and industry through the fostering of innovative talents.
The five campuses of Xuhui, Minhang, Luwan, Qibao and Fahua of the university take up an area of around 400 hectares, housing a variety of activities from student life, athletics, and entertainment to research with laboratory facilities among the best in China.
SJTU libraries provide students with over 3.08 million paper books, over 5,000 periodicals and 3.05 million electronic documents. The campus network, with an enormous amount of resources, has become a digital platform for faculty members and students to enhance their work and studies.
SJTU has a large number of student associations so that its students can fully exert their abilities and make the most of their time here. There are more than 150 student associations, covering almost every area of interest that students might have. And SJTU students have won numerous awards in international competitions on science, engineering innovation, art and athletics.
Strong faculty and academic programs
SJTU boasts a great number of experts and scholars with international prestige. It now has fifteen members of the Chinese Academy of Sciences, nineteen members of the Chinese Academy of Engineering, eighty eight accredited professors and chair professors of "Cheung Kong Scholars Program," fifty nine recipients of National Science Funds for Outstanding Youth, sixteen leading scientists of the National Program 973, six scientists of the Key Scientific Research Program and seven winners of the National Master Teacher Award.
SJTU now has 20,300 full-time undergraduate students, 19,100 master degree candidates, 9,000 Ph.D. candidates and 5,500 overseas students. It also boasts twelve state-level teaching bases and talent cultivation bases. Fourty courses have been recognized as National Model Courses, and cumulatively, twenty eight Ph.D. dissertations have been awarded National 100 Excellent Doctoral Dissertations.
Leading research
With twenty five schools in various disciplines, SJTU has forty nine national key disciplines, nine national key academic programs at the primary discipline level, eleven national key academic programs at the sub-discipline level, one state laboratory (under construction), six state key laboratories, four state key engineering research centers and two open laboratories of the National Project 863 for the High-Tech Network.
SJTU is constantly strengthening its academic innovation. Winning 23 awards for National Science and Technology in 2008 and 2009, SJTU is making pivotal breakthroughs in its scientific research. Its number of SCI-indexed and EI-indexed papers ranked nationally among top 4 from 2004 to 2008. The number of patents applied ranked among top 3. Annual research funds, from 2005 to 2009, obtained by SJTU exceeded one billion yuan.
International perspective
SJTU students have wide access to international opportunities – with the rapidly expanding portfolio of international exchanges, students and faculty of a world-class university have vast opportunities to meet and visit top universities all around the globe. University of Michigan, MIT, Georgia Tech, Cornell and research institutes such as RAND are only a few among the list of institutions and organizations in close cooperation with SJTU.
During 2009, SJTU received over 6,600 overseas visitors, hosted 54 international academic conferences and engaged 96 long-term foreign experts. In addition, there are more than 5,500 overseas students coming to SJTU each year.
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redpajama_set_name: RedPajamaCommonCrawl


Raymond's Real Feels incorporates indie-rock, folk and electronic influences to both riveting original music and fresh takes on recognizable songs by artists such as Bob Dylan, Thom Yorke, Bon Iver, Paul Simon and more. Regardless of material, the band's improvisational, free-wheeling, "keep-you-on-the-edge-of-your-seat" approach to the music makes each experience with Real Feels a new and captivating experience. It's no surprise that they are one of the hottest, up-and-coming groups in jazz today.


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Dover base celebrates Air Force Outstanding Unit Award
Dec 14th, 2017 · by Mike Finney · Comments:
Leadership from the 436th Airlift Wing receives streamers commemorating the wing's recent Air Force Outstanding Unit Award on Wednesday. The streamers will be displayed on all group and squadron guidons to reflect the achievement. (U.S. Air Force photo/Roland Balik)
DOVER — Col. Ethan Griffin, 436th Airlift Wing commander, said teamwork and dedication to success led to the wing receiving the Air Force Outstanding Unit Award throughout the period of Oct. 1, 2015, to Sept. 30, 2017, on Wednesday.
Leadership from the 436th Airlift Wing received streamers commemorating the Air Force Outstanding Unit Award, the wing's 19th overall since the honor was established in 1954 as the first independent Air Force decoration.
"This award was earned by each and every member of Team Dover and is a reflection of the 'Dover Experience,' which begins from the moment you pass through the gate," said Col. Griffin. "It recognizes the expertise and precision of our aviators who take the 'Dover Tail Flash' all over the world, carrying the reputation of our hometown and our base on their wings."
The AFOUA is awarded to U.S. Air Force units that demonstrate exceptionally meritorious service or outstanding achievement of national or international significance, combat operations against an armed enemy of the United States, or military operations involving conflict with or exposure to hostile actions by an opposing foreign force.
"The unmatched work ethic of our maintainers who labor through the heat of summer and cold of winter to keep our aircraft safe and flying is what won this award," Col. Griffin said. "The hospitality, professionalism and attention to detail of our mission support staff was vital to setting our Wing apart from our sister units."
The AFOUA was last awarded to the wing for the period of Jan. 1, 2013, to Sept. 30, 2014. It marks the 19th AFOUA the 436th Airlift Wing has earned.
"The AFOUA acknowledges the power of the community we have built here within Team Dover," Col. Griffin said. "What we view as normal and day-to-day will now be seen as the standard for the rest of the Air Force to meet."
The award citation recognized the 436th Airlift Wing for its operation of the Department of Defense's largest aerial port, flying more than 8,900 C-5M Super Galaxy and C-17 Globemaster III sorties and delivering more than 196 million pounds of cargo and transporting 37,000 passengers.
Additionally, during this time period, the wing conducted the Air Force's largest runway reconstruction project worth $114 million, which replaced 90-year-old infrastructure with new materials extending the life expectancy by at least 50 years.
During the reconstruction, eight C-5Ms and more than 150 maintenance personnel deployed to Joint Base McGuire-Dix in Lakehurst, New Jersey.
"Despite significant logistical challenges, the wing achieved Air Mobility Command's highest C-5M home station departure reliability rate and beat command standards by 21 percent," read the award citation.
Dover AFB is home to more than 11,000 airmen and joint service members, civilians and families. Its' personnel are responsible for global airlift aboard assigned C-5M Super Galaxy and C-17 Globemaster III aircraft.
Additionally, the 436th Airlift Wing serves as host for key partners such as the Air Force Reserve's 512th Airlift Wing, the Air Force Mortuary Affairs Operations, the Armed Forces Medical Examiner System and the Joint Personal Effects Depot, jointly responsible for the dignified return of fallen American service members.
Delaware State News staff writer Mike Finney can be reached at mfinney@newszap.com.


Owen Wesley Siler was born on 10 January 1922, in Seattle, WA. He was graduated from Santa Maria High School, Santa Maria, CA in 1938 and received an Associate Arts Degree from Santa Maria Junior College in 1940. Appointed a Cadet on 19 July 1940, he graduated from the U.S. Coast Guard Academy, New London, CT with a Bachelor of Science Degree in Engineering and with a commission as Ensign on 9 June 1943, the usual four year-curriculum having been shortened because of World War II.
From the Academy he was ordered to combat duty in the Pacific. On board the assault troop transport USS Hunter Liggett (APA-14) he took part in the invasion of Bougainville (Nov. 1943) and other Pacific landings. During his two years of duty in that transport, he served in various billets including that of Gunnery Officer, Assistant Navigator, and Deck Watch Officer. He was then transferred to the assault troop transport USS Bayfield (APA-33) in July 1945. While on board he participated in the occupation of Northern Honshu, Japan, following the surrender of the Japanese.
On returning to the United States in April 1946, he served briefly as Personnel Officer in the Manning Section of Alameda Training Station. CA and then served the remainder of the year as Navigator of the cutter Taney out of Alameda. By January 1947 he was stationed as Communications Officer at the 11th Coast Guard District office at Long Beach, CA.
Assigned as a student aviator in June of 1947, he took his flight training at the Naval Air Training Bases at Corpus Christi TX and at Pensacola, FL. After graduating from the latter with the designation of Coast Guard Aviator on 28 July 1948, he performed his first aerial patrols and search and rescue missions out of the Coast Guard Air Station, Port Angeles, WA. Following a course of advanced training at the Naval All Weather Flying School at Corpus Christi from February to May of 1952, he was next stationed for two years at the Coast Guard Air Detachment Barbers Point, HI. He served his next tour of duty at Coast Guard Headquarters. Washington DC as Aide to the Commandant, as well as Administrative Pilot from August 1954 to July 1959. During the following three years he commanded the Coast Guard Air Station at Corpus Christi.
From August 1962 to August 1964, he served as Chief, Search and Rescue Branch, at the 17th Coast Guard District Office in Juneau. AK. From there he transferred to the Coast Guard Air Station, Miami. FL where he first served as Executive Officer for a year and then as Commanding Officer for a year. Under his command that station received a Coast Guard Unit Commendation for the Cuban Exodus operations during October and November of 1965. During this period the Air Station was involved in 85 assistance cases and, with other Coast Guard units, helped deliver 8,100 refugees to Key West.
After a year of student work at the National War College from August 1966 to June 1967, he began his second tour of duty at Coast Guard Headquarters in the post of Chief, Administrative Management Division. In February 1968 he was named Assistant Chief of Staff for Management, and on 1 July 1969, assumed the post of Deputy Chief of Staff. Meanwhile, he earned an M.S. Degree in International Affairs from George Washington University in 1968.
By nomination of the President in January 1971, and the approval of the Senate, the then Captain Siler was appointed to rank as permanent Rear Admiral from 1 July 1971. Subsequently, he began his first assignment as flag officer in the post of Commander, Second Coast Guard District, St. Louis, MO. For his service during that tour of duty, he received the Meritorious Service Medal in 1972 for directing successful efforts to avert a potential major disaster near Louisville. KY, where a barge loaded with deadly chlorine had smashed into a dam. In May 1974 he received the Legion of Merit for his overall performance as Second District Commander.
Following his nomination by President Nixon on 4 February 1974, Rear Admiral Siler succeeded retiring Admiral Chester R. Bender, USCG, to the post of Commandant of the US Coast Guard with the rank of full four-star Admiral, effective on 1 June 1974. He accomplished many significant things during his tenure as Commandant. The first month of his term saw the end of the 34-year old Ocean Station program. He instituted a Minority Recruiting Program that substantially increased the representation of minorities in the service. Moreover, the distinctively unique new Coast Guard uniform, known as the Bender Blues, became the mandatory uniform for all service members under Siler's term. Also, he was instrumental in arranging to have women cadets admitted to Coast Guard Academy and the first female officers sent to flight training. Admiral Siler also prepared his Service for its new responsibilities with Congress' passage of the Fisheries Conservation and Management Act of 1976, which increased the Coast Guard's area of jurisdiction along the nation's coastline to over two million square miles.
Another significant occurrence was the start, in December 1975, of the processing of applications for the construction of deep-water ports in the Gulf of Mexico, which will give deep-draft tankers access to the United States and helped reduce the nation's fuel bills. In the fall of 1975, the Coast Guard again demonstrated its polar expertise, when three Coast Guard cutters and their helicopters escorted 15 barges around Point Barrow to the North Slope region with more than $500 million worth of equipment, including assembled seven-story modular buildings. Then, in January 1976, the new polar icebreaker, the USCGC Polar Star, was commissioned. In 1975 strike team members, at the request of the Government of Japan, flew to Singapore to help stem the flow of oil from the grounded tanker SHOWA MARU. A year earlier, in August 1974, the Chilean government utilized similar assistance when the tanker Metula went aground in the Strait of Magellan. Strike team members also assisted in the February 1976 cleanup operations following a 250,000 gallon oil spill in Chesapeake Bay. Described as the worst in the bay's history, it occurred when a Stuart Petroleum Company barge ran aground in rough weather.
Earlier, in 1975, through chemical analysis or "oil fingerprinting" of samples from 34 ships that were tested at the Coast Research and Development Center at Groton, CT, the Coast Guard was able to identify the polluter responsible for dumping 40,000 gallons of oil in the Florida Keys. This resulted in the arrest of the master of the Liberian flag tanker Garbis. In November 1974 the Coast Guard blockaded 275 miles of Florida's east coast as a means of curbing drug smuggling, this was the first time American waters had been blockaded since Prohibition. On 4 July 1976, the Coast Guard cutter Eagle proudly led the world's tall ships in a Bicentennial Salute to America at New York City as part of OPERATION SAIL.
Strong new emphasis was placed on the Coast Guard's Marine Environmental Protection (MEP) program during the '70s; much of it during Admiral Siler's tenure as Commandant. A plethora of oil tanker mishaps during the winter of '76 - '77 brought the effectiveness of the MEP program under close scrutiny by a concerned media and the public. Although the grounding of the Argo Merchant (a Liberian tanker that spilled some 7.5 million gallons of oil off Nantucket Island) focused new attention on the MEP program and sparked Presidential initiatives for pollution prevention, much had already been accomplished and the groundwork for implementing the President's policy already existed. Oil fingerprinting to identify polluters and airborne surveillance methods had been developed. The use of highly-skilled and specially-trained Coast Guard personnel, ready to respond to pollution incidents (strike teams) also had been greatly expanded. In fact these strike teams have an international reputation. Responding to requests from the governments of Norway, Chile, Japan, Colombia, and Ecuador, they fought pollution in the North Sea, Straits of Magellan and Malacca, and the Pacific Ocean.
Following the Carter initiatives in March 1977, new regulations were proposed which would require, for certain vessels, double bottoms, segregated ballast, improved emergency steering systems, inert gas systems, and back-up radar and collision avoidance equipment. At the February 1978 International Convention on Tanker Safety and Pollution Prevention in London, the only deviation from the U.S. proposals agreed to was the substitution of crude oil washing systems and protectively-located segregated ballast tanks. This was instead of double bottoms for new tankers and the exemption of small product carriers from the segregated ballast requirement. The measures agreed to at the Conference were contained in new protocols, one of which supplemented the l974 Safety of Life at Sea Convention and a second which incorporated and modified the 1973 Marine Pollution Convention.
The prevention aspect of environmental concerns also had their affect on the Coast Guard's commercial vessel safety and port safety programs during the four years. Increased emphasis on pollution prevention greatly expanded workloads in this field. In addition to the proposed regulations already mentioned and legislation enacted prior to the Argo Merchant grounding, vessel traffic services to reduce the risk of collisions, groundings, and rammings became operational in Puget Sound, Prince William Sound in Alaska, San Francisco, Houston- Galveston, Berwick Bay, and New Orleans. These increased demands required the Coast Guard, not only assume greater workloads for personnel, but also to identify more efficient means of utilizing those personnel and their equipment. An excellent example of this was the combining of nearly forty Captains of the Port and Marine Inspection Offices into new Marine Safety Offices that are better able to meet the related needs of environmental protection and maritime safety.
While public awareness of the Coast Guard and its missions was greatly enhanced during the Siler era, one of the major challenges faced by the service provided the best vehicle for image-enhancement. The preparation for and implementation of the new 200-mile fishery conservation zone created by the Fishery Conservation and Management Act of 1976 (FCMA), not only placed increased demands on personnel and equipment, it also thrust the Coast Guard into the national media spotlight. Despite some pre-FCMA phobia concerning possible confrontations over fishing on the high seas within the zone and some skepticism of the Coast Guard's ability to do the job, implementation of the Act went smoothly. During the first year of enforcement, approximately 2400 boarding of foreign and domestic vessels were conducted. More than 500 citations for minor violations were issued and 250 civil penalty actions were initiated in cases of more serious infractions. During the early months of enforcement, three foreign vessels were seized resulting in the assessment of criminal and civil fines totaling $589,000. In addition in February 1978, a Japanese fishing vessel was seized for deliberately trawling in a closed area. Sixteen tons of illicit catch from another foreign fishing vessel also was seized in early 1977.
Perhaps the most apparent result of the US fisheries management regime was the decline in observed foreign fishing activities. The foreign fishing effort was down to a twelve-nation operation with five fewer countries and 35 percent fewer vessels involved since the FCMA went into effect. Overall, the attitude of foreign fishermen toward the regulations was excellent and it was apparent that they were trying to comply with the FCMA and cooperate with enforcement personnel. During March/April 1977, the first two months of enforcement, violations were detected on 50 percent of the foreign fishing vessels boarded. This percentage decreased to only about 13 percent (and these were only minor infractions of the regulations) in July/August. Thus, with few exceptions, the trend in conformity with the law continued.
Yet another challenging expansion of its responsibilities during the Siler years was the Coast Guard's growing involvement in drug interdiction activities. Although anti-smuggling efforts were a historical Coast Guard role, there had been little modern activity in this area until 1976. This was when increased cooperation with the Drug Enforcement Administration and the US Customs Service saw Coast Guard interdiction efforts rise significantly. In the three years preceding 1976, Coast Guard efforts resulted in the seizure of 24 vessels and narcotics valued at 72 million dollars. But in 1976 alone, 26 vessels and 130 million dollars worth of narcotics were seized. The Service's renewed emphasis on drug interdiction operations continued throughout 1977 and by year's end, Coast Guard units had seized or participated in the seizure of 52 vessels and illegal drugs with a street value of over 400 million dollars.
In a related area, a task group for the prevention and prosecution of vessel hijackings was formed in October 1977. This group developed procedures for the identification and prosecution of vessel hijacking cases and to promulgate periodic advisories suggesting procedures that yachtsmen can take to minimize susceptibility to hijacking. Although the actual number of confirmed hijackings is low, Admiral Siler has stated that "any incident involving the taking and controlling of a vessel and the personnel aboard by force or threat of force is a serious matter for oar concern and action." In addition to creating the task group, he issued new guidelines intended to make the Coast Guard more responsive to vessel theft victims and the Service now will provide, within its jurisdictional authority and resource capability, as much assistance in stolen-vessel cases as possible.
The shift in emphasis among Coast Guard programs was visible in a unique way in the aids to navigation program. Maintenance and operation of aids to navigation traditionally had been a manual operation. For many decades, the manned lighthouse or lightship was the major component of the ATON program. This function, however, had been undergoing a continuous modernization process and became highly automated with most lighthouses being unmanned and most lightships being replaced by Large Navigation Buoys or offshore light structures. In addition the role of radio aids rapidly expanded. This automation process enabled the redistribution of personnel to more critical areas. In 1974 Loran-C was designated as the government-provided primary marine navigation system for use in the Coastal Confluence Zone (CCZ) of the United States and the Great Lakes. The Coast Guard moved steadily toward completion of the Loran-C system in the CCZ and the Great Lakes through the Loran-C National Implementation Plan. This plan was part of the Department of Transportation's National Plan for Navigation.
The chains serving the United States' West Coast, Canadian West Coast, and the Gulf of Alaska were completed and in operation. These were the first LORAN chains constructed specifically for civil use. The Canadian Coast Guard constructed and operated a Loran-C station at Williams Lake, British Columbia. This station operated with two United States stations to form the Canadian West Coast LORAN-C Chain and represented the first time in which another country, at its own initiative and expense, joined with the United States in providing LORAN-C service. There are several LORAN-C stations established on foreign soil to meet Department of Defense requirements, however, these stations were operated at the expense of the United States.
When he became Commandant, one of the first aims of Admiral Siler was to revitalize the Coast Guard's aging capital plant. These became increasingly difficult to manage and costly to maintain. Six of the High-Endurance cutters had been built in 1936 and five Medium- Endurance cutters had been around since the mid-1940s. Most of the medium-range aircraft were nearing the end of their operational effectiveness. His efforts to revitalize the fleet and reverse the trend were successful in a period when strong efforts were being made to reduce federal expenditures. The urgency of the Coast Guard's needs was presented so that the Department of Transportation, the Office of Management and Budget, and Congress clearly recognized it. This resulted in the commencement of a historic replacement program. This included two new 399-foot polar icebreakers and the construction of new 140-foot tugs with domestic icebreaking capabilities. In addition a new class of 160-foot construction tenders were built. These vessels were nearly 70 percent faster, had a smaller crew, and required 10 percent less maintenance than the tenders they replaced. Contracts for four new 270-foot Medium-Endurance cutters were awarded. New 41-foot motor lifeboats and 32-foot ports and waterways boats were also brought into the inventory. In addition the service refurbished the 180-foot buoy tenders and the 95-foot patrol boats.
Search and Rescue stations at Rockland, ME; Menemsha, MA; Fort Myers, FL; and Bayfield, WI were renovated, while a new station was constructed at Provincetown, MA. The stations at St. Inigoes on Chesapeake Bay and Destin and Fort Lauderdale, FL also became operational. These latter improvements resulted from Admiral Siler's feelings that "the time has come when we must address the shore plant. Continued deterioration in this area forces expenditure of funds on inefficient 'patch work' repairs, detracts from operational efficiency, and forces oar personnel to continue to live and work in an unsatisfactory, unwholesome, and often unsafe environment."
The role of women in the Coast Guard also expanded dramatically during Admiral Siler's four years as Commandant. Under his direction the Coast Guard did away with many of the old barriers to the career fields women could enter. The Coast Guard Academy at New London, CT was the first of the military service academies to announce acceptance of women cadets. In January 1976 the first female Coast Guard flight student reported for training and earned her wings in early 1977. A second woman pilot completed her flight training later that year.
A significant development in the role of women during the Siler era was the decision to assign women officers and enlisted personnel to both seagoing and isolated billets. Four officers and twenty enlisted women served aboard the 378-foot High Endurance Cutters Gallatin and Morgenthau. Many shore stations also had mixed-gender crews.
There were numerous other accomplishments during the Siler "Commandancy." These include the licensing of deepwater ports off Louisiana and Texas; the development, in 1976, of the most comprehensive regulatory package of safety standards ever devised for recreational boats; the expanded use of Coast Guard Auxiliarists for Search and Rescue and regatta patrols; enlightened employment of Reservists to augment regular forces in normal missions and emergencies; an ever-increasing role in international affairs, including negotiations on the Law of the Sea and participation in IMCO, the Inter-governmental Maritime Consultative Organization, which is the maritime branch of the United Nations; and the day to day struggle to remain "Always Ready" whatever the mission.
The traditional view of the Coast Guard as simply the ''humanitarian Service," the waterborne rescue agency of the federal government, did not disappear during the Siler years, but it was altered. Although search and rescue was still the "bread and butter" mission of the Coast Guard, legislators and the general public attained a broader perspective of the service. It came to be recognized as a leader in marine environmental protection, a law enforcement agency with which to be reckoned, a conservation-conscious protector of our marine resources and as a major force in a dozen other roles the public was theretofore unaware.
On 30 May 1978 Admiral Owen W. Siler closed his thirty-five year Coast Guard career and retired. One of the characteristics of the Siler era was the Coast Guard's rapid growth in responsibilities and the resultant shift in program emphasis. While experiencing only modest growth in personnel, the service, under Admiral Siler's leadership, was successful in meeting its expanding missions by redirecting resources, improving techniques and making maximum use of personnel and equipment.
Overall, the Siler era was four years of change and expansion, of challenge and growth, marked by active accomplishment. Admiral Owen Wesley Siler left the Coast Guard a legacy of inspired leadership of which he was justifiably proud.
The following is a resume of Admiral Siler's appointments in rank: Cadet, July 19, 1940; Ensign, June 9, 1943; Lieutenant (jg), April 1, 1944; Lieutenant, November 1,1945; Lieut. Commander, August 26, 1952; Commander. July 1, 1959; Captain, July 1, 1965; Rear Admiral, July 1, 1971; Admiral, June 1, 1974. Furthermore, Admiral Siler's medals and awards included: Coast Guard Distinguished Service Medal (1977); Secretary of Transportation's Award for Outstanding Achievement (1977); Legion of Merit (1974); Meritorious Service Medal (1972); Coast Guard Unit Commendation (1965); Norwegian Order of St. Olav, Commander's rank (1976); World War II campaign service medal and ribbons--American Defense Service; American Area; Asiatic-Pacific; World War II Victory; Navy Occupation Service (Asia); also the National Defense Service Medal (Korean and Vietnam).